Predicting storage array capacity

ABSTRACT

An information handling system includes a storage system and a remote processing system. The storage system includes a storage array and a local storage usage predictor. The local storage usage predictor receives usage information from the storage array, and predicts a first usage prediction for the storage array based upon the usage information. The remote processing system includes a remote storage usage predictor remote from the storage system. The remote storage usage predictor receives the usage information and to predicts a second usage prediction for the storage array based upon the usage information.

FIELD OF THE DISCLOSURE

This disclosure generally relates to information handling systems, andmore particularly relates to storage array capacity in an informationhandling system.

BACKGROUND

As the value and use of information continues to increase, individualsand businesses seek additional ways to process and store information.One option is an information handling system. An information handlingsystem generally processes, compiles, stores, and/or communicatesinformation or data for business, personal, or other purposes. Becausetechnology and information handling needs and requirements may varybetween different applications, information handling systems may alsovary regarding what information is handled, how the information ishandled, how much information is processed, stored, or communicated, andhow quickly and efficiently the information may be processed, stored, orcommunicated. The variations in information handling systems allow forinformation handling systems to be general or configured for a specificuser or specific use such as financial transaction processing,reservations, enterprise data storage, or global communications. Inaddition, information handling systems may include a variety of hardwareand software resources that may be configured to process, store, andcommunicate information and may include one or more computer systems,data storage systems, and networking systems.

SUMMARY

An information handling system may include a storage system and a remoteprocessing system. The storage system includes a storage array and alocal storage usage predictor. The local storage usage predictor mayreceive usage information from the storage array and predict a firstusage prediction for the storage array based upon the usage information.The remote processing system may include a remote storage usagepredictor remote from the storage system. The remote storage usagepredictor may receive the usage information and to predict a secondusage prediction for the storage array based upon the usage information.

BRIEF DESCRIPTION OF THE DRAWINGS

It will be appreciated that for simplicity and clarity of illustration,elements illustrated in the Figures have not necessarily been drawn toscale. For example, the dimensions of some of the elements areexaggerated relative to other elements. Embodiments incorporatingteachings of the present disclosure are shown and described with respectto the drawings presented herein, in which:

FIG. 1 is a block diagram illustrating a storage management systemaccording to an embodiment of the current disclosure;

FIG. 2 is a flowchart illustrating a method for predicting storagecapacity in a storage system according to an embodiment of the currentdisclosure;

FIG. 3 is a flowchart illustrating a method for making a local capacityprediction according to an embodiment of the current disclosure;

FIG. 4 is a flowchart illustrating a method for making a local capacityprediction according to an embodiment of the current disclosure; and

FIG. 5 is a block diagram illustrating a generalized informationhandling system according to another embodiment of the currentdisclosure.

The use of the same reference symbols in different drawings indicatessimilar or identical items.

DETAILED DESCRIPTION OF DRAWINGS

The following description in combination with the Figures is provided toassist in understanding the teachings disclosed herein. The followingdiscussion will focus on specific implementations and embodiments of theteachings. This focus is provided to assist in describing the teachings,and should not be interpreted as a limitation on the scope orapplicability of the teachings. However, other teachings can certainlybe used in this application. The teachings can also be used in otherapplications, and with several different types of architectures, such asdistributed computing architectures, client/server architectures, ormiddleware server architectures and associated resources.

FIG. 1 illustrates a storage management system 100 including a storagesystem 110 and a remote storage manager 120. Storage system 110 includesa storage array 112, a local capacity predictor 114, a policy agent 116,a notification manager 118, an event recorder 120, and an optimizer 122.Remote storage manager 130 includes a remote capacity predictor 132. Ina typical storage management system, alerts are provided when a storageresource, such as a disk drive in a storage array, is reaching an alertthreshold, such as a storage capacity threshold or a data bandwidththreshold. However, such last-minute alerts typically do not allow forenough warning time to increase the storage capacity of the storagearray in order to prevent running out of storage space in the storagearray. This can be somewhat mitigated by setting lower storage capacitylimit and data bandwidth limit thresholds. However, setting lower limitsmay have the unintended consequence of unnecessarily increasing thenumber of alerts that are provided by the storage management system.

Various machine learning and event and threshold modeling may beutilized to attempt to improve the predictions provided by the alerts.In particular, simple modeling, such as linear regression modeling, maybe used, but such simple modeling is typically not sufficient to accountfor multiple scenarios and complex event modeling. More complex modelingmay be utilized to account for more varied scenarios and more complexevent modeling, but such solutions typically require huge data andprocessing capacity which then limits the usable storage space andprocessing capacity of the storage management system. Moreover, suchcomplex models do not typically provide real-time predictions.

In a particular embodiment, a hybrid prediction model is applied tostorage management system 100, where a lightweight predictive model isinstantiated on storage system 110, and a more complex, resourceintensive predictive model is instantiated at remote storage manager130. In particular, the local lightweight predictive model instantiatedon storage system 110 utilizes a relatively small history data set, andsimple predictive algorithms to provide real-time short term predictionsof the data storage capacity of storage array 112 and of data bandwidthfor the storage array. The lightweight predictive model is well adaptedto providing emergency use case predictions, such that an administratorof storage management system 100 for can take short-term remedialactions, such as powering on additional storage servers, migratingworkloads, suspending inactive workloads, or the like.

The lightweight predictive model may utilize linear regression modeling,auto regression (AR), moving average (MA), auto-regressive integratedmoving average (ARIMA), or the like, which are known to providesatisfactory results for short term predictions of the needs of storagemanagement system 100. In a particular embodiment, the lightweightpredictive model utilizes a variety of different algorithms to determinea best fit for the data at hand. As such, events that occur that arelocal to storage system 100 can be quickly accounted for andincorporated into determining the best fitting algorithm.

Examples of local events include installation or removal of hard drives,the failure of one or more hard drive, the reallocation of storagepartitions, and the like. Here, prediction timeframes may be providedfor daily update, weekly update, hourly update, or the like. Thus thelightweight predictive model is termed the “lightweight” both in termsof the type of algorithm utilized, and in terms of the amount ofprocessing resources needed by storage system 110 to implement thealgorithm. As such, storage management system 100 obtains good shortterm storage prediction performance without unnecessarily burdening theprocessing resources of storage system 110.

The resource intensive predictive module is well adapted to providinglong term storage predictions, such as purchasing and installingadditional storage assets, or the like. Here, greater processingresources outside of storage system 110, such as a network or datacenterserver, a cloud processing system, or the like, are utilized to do morecomplex predictive processing to more accurately model the behavior ofstorage system 110. Here, it will be understood that the processingcapacity are greater, and the time constraints are more relaxed ascompared with the lightweight predictive model. The resource intensivepredictive model may utilize long-short term memory (LSTM) modeling, agradient boosting framework such as XGBoost or the like, or other moreresource intensive predictive models, as needed or desired. Further, theresource intensive predictive model may utilize more extensive factors,such as data reduction percentages, snap numbers, snap size, averagesnapshot retention, replication session numbers, replication averagerecovery point objective (RPO), I/O patterns, and the like. Here,prediction timelines may be provided for monthly update, quarterlyupdate, or the like

Returning to FIG. 1, storage array 112 represents the data storage mediaof storage system 110, such as hard disk drives (HDDs), solid statedrives (SSDs), tape backup, or other storage media as needed or desired.Storage array 112 may represent storage media in accordance with one ormore data storage standards, such as SCSI storage devices, FibreChannelstorage devices, or the like. Local capacity predictor 114 implementsthe lightweight predictive model to generate quick short term capacitypredictions for storage system 110. Policy agent 116 defines policyconditions for the predictions provided by local capacity predictor 114.

In particular, policy agent 116 defines the interval for collecting dataon the condition of storage array 112, the prediction frequency, thedefault predictive model utilized in making the short term prediction,thresholds such as data capacity thresholds or bandwidth capacity,optimization policies such as a number of optimization attempts to try,the order of use of the various predictive models, conditions fordetermining the optimized predictive model, optimization order, theoptimization condition, and the like. A table of example policyconditions is given in Table 1, below.

TABLE 1 Example Policy Conditions Example Policy Data CollectionInterval Per day, per hour Prediction Period Next week, next day DefaultModel Linear Regression Evaluation Threshold Model Evaluation passedwhen MSE value is smaller 1.5 Optimization policy Optimization AttemptsNo more than 3 attempts Optimization Condition Optimization triggeredwhen CPU utilization is less than <60% Optimization Order Rebuilddefault model with a subset of history data. If it does not fit, useARIMA model

Notification manager 118 operates to generate notifications based uponthe short term prediction. In a particular embodiment, notificationmanager 118 also generates notifications based upon the long termprediction as described above. Event recorder 120 operates to record theevents in storage system 110 that affect the local prediction, such asthe addition or removal of storage media from storage array 110, thedeletion from, or migration to the storage array of large storageobjects, or the like. Optimizer 122 optimizes local capacity predictor114 when the selected model does not pass the evaluation.

In particular, when the selected model does not pass the evaluation,optimizer 122 may operate to narrow the range of historical dataevaluated by local capacity predictor 114, to determine if system eventshave occurred which might cause the model to fail the evaluation, toselect different predictive algorithms, or the like, in order to arriveat a better optimization from the local capacity predictor. Remotecapacity predictor 132 implements the resource intensive predictivemodel as described above.

FIG. 2 illustrates a method for predicting storage capacity in a storagesystem, starting at block 200. A short term storage capacity predictionis provided by a capacity predictor that is local to the storage systemin block 202, and a long term storage capacity prediction is provided bya capacity predictor that is remote from the storage system in block204. Notifications based upon the predictions made in blocks 202 and 204are made when the respective predictions indicate a capacity shortfallin block 206, and the method ends in block 208.

FIG. 3 illustrates a method for making a local capacity prediction,starting at block 300. The data for a storage array is collected inblock 302. For example, the storage utilization or available storageutilization, the data bandwidth, and the like, can be collected inaccordance with a policy agent, such as at a pre-defined interval, for apredefined duration, or the like. A local predictive model is built inblock 204. For example, the default predictive model may include alinear regression algorithm. The selected model is evaluated with thecollected data in block 306. For example, the selected model may beevaluated a root-mean squared value (RMS), a root-mean square error(RMSE) value, an R2 value, or the like. A decision is made as to whetheror not the model passed the evaluation in decision block 308. Forexample, an RMS value may be determined to have passed the evaluationwhen the value is within a threshold, where the R2 value is greater thana minimum value, or the like.

If the model passed the evaluation, the “YES” branch of decision block308 is taken, a short term capacity prediction is made in block 310, theshort term prediction is passed to a notification manager in block 312,and the method ends in block 314. If the model did not pass theevaluation, the “NO” branch of decision block 308 is taken and adecision is made as to whether or not the model matches a policy inblock 316. For example, a policy agent may determine if a number ofattempts to determine a prediction value exceeds a threshold, or maydetermine that a processor utilization is less than another threshold.If the model matches the policy, the “YES” branch of decision block 316is taken, the model is optimized in block 318, and the method returns toblock 304. If the model does not match the policy, the “NO” branch ofdecision block 316 is taken, a notification that no prediction was madeis sent in block 320, and the method ends in block 314.

FIG. 4 illustrates a method for making a local capacity prediction,starting at block 400. The data for a storage array is collected inblock 402. A remote predictive model is built in block 204. For example,the default predictive model may include a LSTM algorithm. The selectedmodel is evaluated with the collected data in block 406. For example,the selected model may be evaluated a root-mean squared value (RMS), aroot-mean square error (RMSE) value, an R2 value, or the like. Adecision is made as to whether or not the model passed the evaluation indecision block 408. For example, an RMS value may be determined to havepassed the evaluation when the value is within a threshold, where the R2value is greater than a minimum value, or the like. If the model passedthe evaluation, the “YES” branch of decision block 408 is taken, a longterm capacity prediction is made in block 410, the short term predictionis passed to a notification manager in block 412, and the method ends inblock 414.

If the model did not pass the evaluation, the “NO” branch of decisionblock 408 is taken and a decision is made as to whether or not the modelmatches a policy in block 416. For example, a policy agent may determineif a number of attempts to determine a prediction value exceeds athreshold, or may determine that a processor utilization is less thananother threshold. If the model does not match the policy, the “NO”branch of decision block 416 is taken, the model is optimized in block418, and the method returns to block 404. If the model does match thepolicy, the “YES” branch of decision block 416 is taken, a notificationthat no prediction was made is sent in block 420, the time interval forthe model is updated in block 422, and the method ends in block 414.

FIG. 5 illustrates a generalized embodiment of an information handlingsystem 500. For purpose of this disclosure an information handlingsystem can include any instrumentality or aggregate of instrumentalitiesoperable to compute, classify, process, transmit, receive, retrieve,originate, switch, store, display, manifest, detect, record, reproduce,handle, or utilize any form of information, intelligence, or data forbusiness, scientific, control, entertainment, or other purposes. Forexample, information handling system 500 can be a personal computer, alaptop computer, a smart phone, a tablet device or other consumerelectronic device, a network server, a network storage device, a switchrouter or other network communication device, or any other suitabledevice and may vary in size, shape, performance, functionality, andprice.

Further, information handling system 500 can include processingresources for executing machine-executable code, such as a centralprocessing unit (CPU), a programmable logic array (PLA), an embeddeddevice such as a System-on-a-Chip (SoC), or other control logichardware. Information handling system 500 can also include one or morecomputer-readable medium for storing machine-executable code, such assoftware or data. Additional components of information handling system500 can include one or more storage devices that can storemachine-executable code, one or more communications ports forcommunicating with external devices, and various input and output (I/O)devices, such as a keyboard, a mouse, and a video display. Informationhandling system 500 can also include one or more buses operable totransmit information between the various hardware components.

Information handling system 500 can include devices or modules thatembody one or more of the devices or modules described below, andoperates to perform one or more of the methods described below.Information handling system 500 includes a processors 502 and 504, aninput/output (I/O) interface 510, memories 520 and 525, a graphicsinterface 530, a basic input and output system/universal extensiblefirmware interface (BIOS/UEFI) module 540, a disk controller 550, a harddisk drive (HDD) 554, an optical disk drive (ODD) 556, a disk emulator560 connected to an external solid state drive (SSD) 562, an I/O bridge570, one or more add-on resources 574, a trusted platform module (TPM)576, a network interface 580, a management device 590, and a powersupply 595. Processors 502 and 504, I/O interface 510, memory 520,graphics interface 530, BIOS/UEFI module 540, disk controller 550, HDD554, ODD 556, disk emulator 560, SSD 562, I/O bridge 570, add-onresources 574, TPM 576, and network interface 580 operate together toprovide a host environment of information handling system 500 thatoperates to provide the data processing functionality of the informationhandling system. The host environment operates to executemachine-executable code, including platform BIOS/UEFI code, devicefirmware, operating system code, applications, programs, and the like,to perform the data processing tasks associated with informationhandling system 500.

In the host environment, processor 502 is connected to I/O interface 510via processor interface 506, and processor 504 is connected to the I/Ointerface via processor interface 508. Memory 520 is connected toprocessor 502 via a memory interface 522. Memory 525 is connected toprocessor 504 via a memory interface 527. Graphics interface 530 isconnected to I/O interface 510 via a graphics interface 532, andprovides a video display output 536 to a video display 534. In aparticular embodiment, information handling system 500 includes separatememories that are dedicated to each of processors 502 and 504 viaseparate memory interfaces. An example of memories 520 and 530 includerandom access memory (RAM) such as static RAM (SRAM), dynamic RAM(DRAM), non-volatile RAM (NV-RAM), or the like, read only memory (ROM),another type of memory, or a combination thereof.

BIOS/UEFI module 540, disk controller 550, and I/O bridge 570 areconnected to I/O interface 510 via an I/O channel 512. An example of I/Ochannel 512 includes a Peripheral Component Interconnect (PCI)interface, a PCI-Extended (PCI-X) interface, a high-speed PCI-Express(PCIe) interface, another industry standard or proprietary communicationinterface, or a combination thereof. I/O interface 510 can also includeone or more other I/O interfaces, including an Industry StandardArchitecture (ISA) interface, a Small Computer Serial Interface (SCSI)interface, an Inter-Integrated Circuit (I²C) interface, a System PacketInterface (SPI), a Universal Serial Bus (USB), another interface, or acombination thereof. BIOS/UEFI module 540 includes BIOS/UEFI codeoperable to detect resources within information handling system 500, toprovide drivers for the resources, initialize the resources, and accessthe resources. BIOS/UEFI module 540 includes code that operates todetect resources within information handling system 500, to providedrivers for the resources, to initialize the resources, and to accessthe resources.

Disk controller 550 includes a disk interface 552 that connects the diskcontroller to HDD 554, to ODD 556, and to disk emulator 560. An exampleof disk interface 552 includes an Integrated Drive Electronics (IDE)interface, an Advanced Technology Attachment (ATA) such as a parallelATA (PATA) interface or a serial ATA (SATA) interface, a SCSI interface,a USB interface, a proprietary interface, or a combination thereof. Diskemulator 560 permits SSD 564 to be connected to information handlingsystem 500 via an external interface 562. An example of externalinterface 562 includes a USB interface, an IEEE 1394 (Firewire)interface, a proprietary interface, or a combination thereof.Alternatively, solid-state drive 564 can be disposed within informationhandling system 500.

I/O bridge 570 includes a peripheral interface 572 that connects the I/Obridge to add-on resource 574, to TPM 576, and to network interface 580.Peripheral interface 572 can be the same type of interface as I/Ochannel 512, or can be a different type of interface. As such, I/Obridge 570 extends the capacity of I/O channel 512 when peripheralinterface 572 and the I/O channel are of the same type, and the I/Obridge translates information from a format suitable to the I/O channelto a format suitable to the peripheral channel 572 when they are of adifferent type. Add-on resource 574 can include a data storage system,an additional graphics interface, a network interface card (NIC), asound/video processing card, another add-on resource, or a combinationthereof. Add-on resource 574 can be on a main circuit board, on separatecircuit board or add-in card disposed within information handling system500, a device that is external to the information handling system, or acombination thereof.

Network interface 580 represents a NIC disposed within informationhandling system 500, on a main circuit board of the information handlingsystem, integrated onto another component such as I/O interface 510, inanother suitable location, or a combination thereof. Network interfacedevice 580 includes network channels 582 and 584 that provide interfacesto devices that are external to information handling system 500. In aparticular embodiment, network channels 582 and 584 are of a differenttype than peripheral channel 572 and network interface 580 translatesinformation from a format suitable to the peripheral channel to a formatsuitable to external devices. An example of network channels 582 and 584includes InfiniBand channels, Fibre Channel channels, Gigabit Ethernetchannels, proprietary channel architectures, or a combination thereof.Network channels 582 and 584 can be connected to external networkresources (not illustrated). The network resource can include anotherinformation handling system, a data storage system, another network, agrid management system, another suitable resource, or a combinationthereof.

Management device 590 represents one or more processing devices, such asa dedicated baseboard management controller (BMC) System-on-a-Chip (SoC)device, one or more associated memory devices, one or more networkinterface devices, a complex programmable logic device (CPLD), and thelike, that operate together to provide the management environment forinformation handling system 500. In particular, management device 590 isconnected to various components of the host environment via variousinternal communication interfaces, such as a Low Pin Count (LPC)interface, an Inter-Integrated-Circuit (I2C) interface, a PCIeinterface, or the like, to provide an out-of-band (00B) mechanism toretrieve information related to the operation of the host environment,to provide BIOS/UEFI or system firmware updates, to managenon-processing components of information handling system 500, such assystem cooling fans and power supplies. Management device 590 caninclude a network connection to an external management system, and themanagement device can communicate with the management system to reportstatus information for information handling system 500, to receiveBIOS/UEFI or system firmware updates, or to perform other task formanaging and controlling the operation of information handling system500. Management device 590 can operate off of a separate power planefrom the components of the host environment so that the managementdevice receives power to manage information handling system 500 when theinformation handling system is otherwise shut down. An example ofmanagement device 590 include a commercially available BMC product orother device that operates in accordance with an Intelligent PlatformManagement Initiative (IPMI) specification, a Web Services Management(WSMan) interface, a Redfish Application Programming Interface (API),another Distributed Management Task Force (DMTF), or other managementstandard, and can include an Integrated Dell Remote Access Controller(iDRAC), an Embedded Controller (EC), or the like. Management device 590may further include associated memory devices, logic devices, securitydevices, or the like, as needed or desired.

Although only a few exemplary embodiments have been described in detailherein, those skilled in the art will readily appreciate that manymodifications are possible in the exemplary embodiments withoutmaterially departing from the novel teachings and advantages of theembodiments of the present disclosure. Accordingly, all suchmodifications are intended to be included within the scope of theembodiments of the present disclosure as defined in the followingclaims. In the claims, means-plus-function clauses are intended to coverthe structures described herein as performing the recited function andnot only structural equivalents, but also equivalent structures.

The above-disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover any andall such modifications, enhancements, and other embodiments that fallwithin the scope of the present invention. Thus, to the maximum extentallowed by law, the scope of the present invention is to be determinedby the broadest permissible interpretation of the following claims andtheir equivalents, and shall not be restricted or limited by theforegoing detailed description.

What is claimed is:
 1. An information handling system, comprising: astorage system including a storage array and a local storage usagepredictor, the local storage usage predictor configured to receive usageinformation from the storage array, and to predict a first usageprediction for the storage array based upon the usage information; and aremote processing system including a remote storage usage predictorremote from the storage system, the remote storage usage predictorconfigured to receive the usage information and to predict a secondusage prediction for the storage array based upon the usage information.2. The information handling system of claim 1, wherein the local storageusage predictor predicts the first usage prediction based upon at leastone of a linear regression model, an auto regression (AR) model, amoving average (MA) model, and an auto-regressive integrated movingaverage (ARIMA) model.
 3. The information handling system of claim 2,wherein the local storage usage predictor is further configured toutilize each of the linear regression model, the AR model, the MA model,and the ARIMA model in predicting the first usage prediction.
 4. Theinformation handling system of claim 2, wherein the local storage usagepredictor is further configured to utilize the linear regression modelas a default prediction model in predicting the first usage prediction,to determine that the first usage prediction has not passed anevaluation criteria, and to utilize a second one of the AR model, the MAmodel, and the ARIMA model to predict a third usage prediction for thestorage array based upon the usage information.
 5. The informationhandling system of claim 1, wherein the local storage usage predictor isfurther configured to determine an event associated with the storagearray, wherein the first usage prediction is further based upon theevent.
 6. The information handling system of claim 5, wherein the eventincludes one of a storage capacity of the storage array increasing, thestorage capacity of the storage array decreasing, an image objectmigrating into the storage array, and the image object migrating out ofthe storage array.
 7. The information handling system of claim 1,wherein the remote storage usage predictor predicts the long term usageprediction based upon at least one of a long-short term memory model anda gradient boosting framework model.
 8. The information handling systemof claim 1, wherein the usage information includes one of a currentstorage capacity of the storage array and a current bandwidth of thestorage array.
 9. The information handling system of claim 8, whereinthe first usage prediction includes one of a storage capacity predictionand a data bandwidth prediction of the storage array.
 10. Theinformation handling system of claim 1, wherein the first usageprediction is for a shorter duration than the second usage prediction.11. A method, comprising: receiving, by a local storage usage predictorof a storage system, usage information from a storage array of thestorage system; predicting, by the local storage usage predictor, afirst usage prediction for the storage array based upon the usageinformation; receiving, by a remote storage usage predictor remote fromthe storage system, the usage information; and predicting, by the remotestorage usage predictor, a second usage prediction for the storage arraybased upon the usage information, wherein the first usage prediction isfor a shorter duration than the second usage prediction.
 12. The methodof claim 11, wherein the local storage usage predictor predicts thefirst usage prediction based upon at least one of a linear regressionmodel, an auto regression (AR) model, a moving average (MA) model, andan auto-regressive integrated moving average (ARIMA) model.
 13. Themethod of claim 12, further comprising: utilizing, by the local storageusage predictor, each of the linear regression model, the AR model, theMA model, and the ARIMA model in predicting the first usage prediction.14. The method of claim 12, further comprising: utilizing, by the localstorage usage predictor, the linear regression model as a defaultprediction model in predicting the first usage prediction; determiningthat the first usage prediction has not passed an evaluation criteria;and utilizing a second one of the AR model, the MA model, and the ARIMAmodel to predict a third usage prediction for the storage array basedupon the usage information.
 15. The method of claim 11, furthercomprising: determining, by the local storage usage predictor, an eventassociated with the storage array, wherein the first usage prediction isfurther based upon the event.
 16. The method of claim 15, wherein theevent includes one of a storage capacity of the storage arrayincreasing, the storage capacity of the storage array decreasing, animage object migrating into the storage array, and the image objectmigrating out of the storage array.
 17. The method of claim 11, whereinthe remote storage usage predictor predicts the long term usageprediction based upon at least one of a long-short term memory (LSTM)model and a gradient boosting framework model.
 18. The method of claim11, wherein the usage information includes one of a current storagecapacity of the storage array and a current bandwidth of the storagearray.
 19. The method of claim 18, wherein the first usage predictionincludes one of a storage capacity prediction and a data bandwidthprediction of the storage array.
 20. An information handling system,comprising: a notification manager; a storage system including a storagearray and a local storage usage predictor, the local storage usagepredictor configured to receive usage information from the storagearray, to predict a first usage prediction for the storage array basedupon the usage information, and to send the first usage prediction tothe notification manager; and a remote processing system including aremote storage usage predictor remote from the storage system, theremote storage usage predictor configured to receive the usageinformation, to predict a second usage prediction for the storage arraybased upon the usage information, and to send the second usageprediction to the notification manager; wherein the notification manageris configured to provide a first notification based upon the first usageprediction and to provide a second notification based upon the secondusage prediction.